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Online newspaper subscriptions

2024-10Artigo científico Acesso aberto
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dc.contributor.authorBelchior, Lúcia Madeira
dc.contributor.authorAntónio, Nuno
dc.contributor.authorFernandes, Elizabeth
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblTaylor & Francis
dc.date.accessioned2024-04-30T02:28:53Z
dc.date.available2024-04-30T02:28:53Z
dc.date.issued2024-10
dc.descriptionBelchior, L. M., António, N., & Fernandes, E. (2024). Online newspaper subscriptions: using machine learning to reduce and understand customer churn. Journal of Media Business Studies, 21(4), 364-387. https://doi.org/10.1080/16522354.2024.2343638 --- %ABS1% ---This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.
dc.description.abstractModelling customer loyalty has been a central issue in customer relationship management, particularly in digital subscription business models. To guarantee news media sustainability, publishers implemented subscription models that need to define successful retention strategies. Thus, churn management has become pivotal in the media subscription business. The present study aims to understand what drives subscribers to churn by performing a Machine Learning approach to model the propensity to churn of online subscribers of a Portuguese newspaper. Two models were developed, tested, and evaluated in two timeframes. The first one considered all Business to Consumer (B2C) subscriptions, and the second only the B2C non-recurring subscriptions. The experimental results revealed important patterns of churners, which allowed the marketing and editorial teams to implement churn prevention and retention measures.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent24
dc.format.extent3815862
dc.identifier.doi10.1080/16522354.2024.2343638
dc.identifier.issn1652-2354
dc.identifier.otherPURE: 89445514
dc.identifier.otherPURE UUID: 16029bfe-2ab2-4e05-b166-f5e0c1ccdf38
dc.identifier.othercrossref: 10.1080/16522354.2024.2343638
dc.identifier.otherScopus: 85191159701
dc.identifier.otherWOS: 001206681500001
dc.identifier.urihttp://hdl.handle.net/10362/166779
dc.identifier.urlhttps://www.scopus.com/pages/publications/85191159701
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001206681500001
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectChurn prediction
dc.subjectonline subscriptions
dc.subjectdata mining
dc.subjectdigital journalism
dc.subjectreader engagement
dc.subjectBusiness and International Management
dc.subjectCommunication
dc.subjectStrategy and Management
dc.subjectSDG 8 - Decent Work and Economic Growth
dc.titleOnline newspaper subscriptionsen
dc.title.subtitleusing machine learning to reduce and understand customer churnen
dc.typejournal article
degois.publication.firstPage364
degois.publication.issue4
degois.publication.lastPage387
degois.publication.titleJournal of Media Business Studies
degois.publication.volume21
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)